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A Bibliographic Exploration: Tackling Heavy Metal Contamination

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06 January 2024

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08 January 2024

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Abstract
Heavy metal pollution poses a significant threat to global populations, particularly impacting human health. Developing countries, characterized by chemical engineering and mining industrial activities, often contribute to diverse heavy metal contaminants. Consumption of water contaminated with heavy metals, prevalent in such regions, can lead to severe health risks, including gastric cancer. Addressing the urgent issue of heavy metal pollution, this paper employs a bibliographic approach to explore the latest literature on heavy metal pollution. The study discusses recent research findings and potential methods for the treatment and mitigation of heavy metal pollution, emphasizing the pressing need for effective solutions to safeguard public health.
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Subject: Environmental and Earth Sciences  -   Environmental Science

1. Introduction

Heavy metal pollution represents a formidable global challenge with far-reaching implications for human health and environmental sustainability [1]. The pervasive nature of heavy metals, coupled with their adverse effects on ecosystems, underscores the urgency of comprehending and mitigating this complex issue [2]. In particular, developing countries, characterized by burgeoning chemical engineering and mining industrial activities, are often at the forefront of heavy metal pollution, contributing to a myriad of contaminants that pose serious threats to both local environments and human well-being [3]. This introduction delves into the multifaceted dimensions of heavy metal pollution, emphasizing its global significance, its impact on human health, and the imperative for effective solutions to address this pressing environmental concern [4].
Heavy metal pollution is a transboundary issue that transcends geographical and geopolitical boundaries, affecting diverse regions across the globe [5]. Anthropogenic activities, such as industrial processes, mining operations, and agricultural practices, release substantial quantities of heavy metals into the environment [6]. These metals, including but not limited to lead, mercury, cadmium, and arsenic, persist in air, water, and soil, thereby entering the food chain and posing risks to both ecological systems and human populations [7].
One of the noteworthy contributors to heavy metal pollution is the industrialization and economic development observed in many developing countries [8]. Rapid industrial growth, marked by chemical engineering processes and extensive mining activities, releases significant amounts of heavy metals into the environment [9]. These activities often occur without adequate environmental safeguards, exacerbating the pollution burden on local ecosystems [10]. As a result, regions with a high concentration of such industries experience elevated levels of heavy metal contamination, impacting the health and well-being of nearby communities [11].
The consumption of water contaminated with heavy metals presents a direct and severe risk to human health [12]. In many developing regions, access to clean and uncontaminated water is a persistent challenge, leading to widespread exposure to heavy metals through drinking water [13]. Chronic exposure to these contaminants has been linked to various health issues, with gastric cancer being a prominent concern [14]. Additionally, heavy metals can accumulate in human tissues over time, leading to long-term health consequences, including neurological disorders, organ damage, and developmental abnormalities, particularly in vulnerable populations such as children and pregnant women [15].
To comprehensively address the intricate facets of heavy metal pollution, this paper employs a bibliographic research approach [16]. By leveraging the extensive database of scholarly works, particularly utilizing platforms like Web of Science, the study undertakes a systematic exploration of the latest literature on heavy metal pollution [17]. The bibliographic method allows for a rigorous examination of existing research, identifying key trends, emerging topics, and gaps in knowledge that warrant further investigation [18].
The study delves into recent research findings on heavy metal pollution, shedding light on innovative approaches and technologies developed to understand, monitor, and mitigate the impact of these contaminants [19]. Researchers and environmental scientists worldwide have been exploring various methods for remediating heavy metal pollution, ranging from phytoremediation and bioremediation to advanced filtration technologies [20]. The paper discusses these strategies, evaluating their efficacy and potential applications in diverse environmental settings [21].
Emphasizing the pressing need for effective solutions, the paper underscores the urgency of addressing heavy metal pollution to safeguard public health and preserve environmental integrity [22]. The ramifications of heavy metal contamination extend beyond immediate health concerns to encompass ecological disruptions, soil degradation, and compromised agricultural productivity [23]. As such, the quest for sustainable solutions demands interdisciplinary collaboration, incorporating insights from environmental science, engineering, medicine, and policy [24].

2. Materials and methods

The bibliographic method followed previous studies with slightly modifications [25,26]. In 2024, our research ventured into an extensive data collection initiative utilizing the esteemed Web of Science database, recognized for its comprehensive subdatabases. This strategic selection aimed to guarantee the reliability and pertinence of the collected data for a diverse audience. Focused on the theme of "Heavy Metal Pollution Solution," our exploration entailed a meticulous analysis of 1000 articles from the widely acknowledged Web of Science database, renowned for its credibility and widespread usage in the academic community.
To visually depict our bibliographic and bibliometric analyses, we utilized the VOSviewer data visualization tool [27,28]. Integrating our data files into VOSviewer empowered us to tailor parameters to align with our research objectives and diverse data sources. Generating maps from web data requires rigorous data-cleaning procedures for accuracy, with VOSviewer playing a pivotal role in streamlining these operations and contributing significantly to robust visualizations.
Following established conventions, our mapping procedures in VOSviewer adhered to default settings from prior scholarly research unless explicitly specified otherwise. Keyword analysis employed a minimum occurrence threshold of "8," while country/region analysis required a minimum of "5" documents for inclusion. Organization-centered analysis considered a minimum of "4" documents for further scrutiny. These standardized parameters ensured a systematic and rigorous bibliometric exploration, enhancing the credibility and validity of our research.

3. Results

Figure 1 serves as an intricate visual representation, offering a comprehensive overview of the intricate landscape associated with heavy metal pollution research. The graphic showcases a multitude of key components, ranging from specific heavy metal elements like "lead," "zinc," and "cadmium" to an assortment of biochemical processes central to the investigation of heavy metal pollution, including but not limited to "sorption," "adsorption," and "kinetics [29]."
In a detailed exploration of the graphic, it becomes evident that the illustration extends its purview beyond mere elements and processes. It delves into a diverse array of methodologies integral to the treatment and remediation of heavy metal pollution. These encompass a spectrum of innovative techniques, such as "biosorption," "bioremediation," and "phytoremediation," each offering a unique approach to mitigating the impact of heavy metals on the environment [30].
The complexity of the heavy metal pollution landscape is further underscored by the inclusion of advanced materials and substances instrumental in pollution control. Notable among these are "nanoparticles" [31,32] and "graphene oxide," [33,34] which represent cutting-edge technologies that hold great potential for enhancing the efficiency and effectiveness of pollution treatment strategies.
The intentional incorporation of such a rich tapestry of elements and approaches within Figure 1 underscores the multidimensional nature of heavy metal pollution research. By presenting a visual synopsis, the figure facilitates a nuanced understanding of the intricate interplay between diverse elements, processes, and methodologies within the broader realm of heavy metal pollution. It acts as a valuable reference, aiding researchers, policymakers, and scholars in navigating the complexities inherent in the pursuit of effective solutions to combat heavy metal pollution and its adverse impacts on the environment and human health.
Figure 1. The primary keywords using VOSviewer, highlighting the key connections between them through connecting lines.
Figure 1. The primary keywords using VOSviewer, highlighting the key connections between them through connecting lines.
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Figure 2 showcases the primary countries engaged in heavy metal pollution research, revealing a nuanced global landscape where China emerges as a central hub of research activities in this domain. In addition to China's prominent position, the figure highlights the significant contributions of other countries, including the United States, Iran, Australia, Singapore, Malaysia, Chile, Brazil, New Zealand, Sweden, Portugal, Morocco, France, Algeria, Egypt, Mexico, Spain, Germany, Italy, Finland, Turkey, Poland, Nigeria, Ukraine, Romania, Serbia, South Korea, the Netherlands, Slovakia, and more.
The distribution of research across these nations underscores the widespread global concern and collaborative efforts to address the challenges posed by heavy metal pollution. Interestingly, the involvement of both developed and developing countries is evident, with developed nations often equipped with advanced research infrastructure and developing nations demonstrating a heightened research motivation fueled by the detrimental impacts of heavy metal pollution on their environments and populations.
This collaborative and diverse international research landscape fosters a synergistic exchange of knowledge and expertise. Developed countries contribute advanced research conditions, while developing nations bring a unique perspective shaped by the urgency of mitigating the harmful effects of heavy metal contamination. The collaborative spirit evident in the joint efforts of these nations promotes scientific progress and a more comprehensive understanding of heavy metal pollution, laying the groundwork for collective solutions to this pressing global challenge.
Figure 2. The primary countries/regions using VOSviewer, illustrating the collaborative connections between them through connecting lines.
Figure 2. The primary countries/regions using VOSviewer, illustrating the collaborative connections between them through connecting lines.
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Figure 3 presents an overview of key organizations involved in heavy metal pollution research, shedding light on the institutions at the forefront of this scientific endeavor. At the epicenter of this research landscape is the Chinese Academy of Sciences, occupying a central and influential position in advancing knowledge and solutions related to heavy metal pollution.
Notably, numerous Chinese organizations have made significant contributions to this field, underscoring the nation's active engagement in heavy metal pollution research. Among these, Zhejiang University, University of Chinese Academy of Sciences, Tsinghua University, Lanzhou University, East China University of Science and Technology, Hong Kong Polytechnic University, Southeast University, Nanjing University, Sun Yat-Sen University, Huazhong Agricultural University, and Tongji University play crucial roles.
Beyond the borders of China, several international institutions also contribute significantly to heavy metal pollution research. Noteworthy examples include Islamic Azad University and the University of Malaya, showcasing a collaborative and global effort to address the multifaceted challenges posed by heavy metal contamination.
The prominence of these organizations, both within China and globally, highlights the collaborative nature of research in this critical field. Their collective efforts underscore the urgency and importance of finding effective strategies to mitigate the impacts of heavy metal pollution on the environment and human health.
Figure 3. The principal organizations using VOSviewer, delineating the collaborative connections between them through connecting lines.
Figure 3. The principal organizations using VOSviewer, delineating the collaborative connections between them through connecting lines.
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4. Discussion

4.1. Navigating the Challenge: Transboundary Issues in Contemporary Heavy Metal Pollution

In contemporary times, heavy metal pollution presents a novel trend characterized by the emergence of transboundary pollution issues [5]. This phenomenon is exemplified by scenarios where one country, despite having extensive chemical engineering and mining facilities, is situated upstream along a river, thus becoming the perpetrator of pollution. Simultaneously, another country, lacking equivalent industrial activities, finds itself downstream and becomes the victim of this transboundary pollution. This asymmetry, where one nation acts as the polluter while another bears the consequences, is a prevalent and challenging aspect of the current heavy metal pollution landscape.
The transboundary nature of heavy metal pollution necessitates a comprehensive examination of international frameworks for pollution governance to effectively address this complex issue [35]. It is imperative to recognize that the consequences of pollution often transcend national boundaries, affecting neighboring countries and ecosystems. Therefore, a holistic approach is required to develop international policies and mechanisms that foster cooperation among nations, regulate cross-border pollution, and allocate responsibilities fairly.
Addressing transboundary heavy metal pollution requires the establishment of collaborative frameworks that promote information sharing, joint monitoring, and coordinated mitigation strategies [36]. This involves developing international agreements, protocols, and mechanisms that enable affected countries to collectively manage and regulate industrial activities contributing to pollution. Additionally, creating incentives for nations to adopt cleaner technologies and sustainable practices can contribute to mitigating transboundary pollution.
Furthermore, the role of international organizations, such as the United Nations Environment Programme (UNEP) [37], in facilitating dialogue and cooperation among nations cannot be overstated. These organizations can serve as platforms for the exchange of best practices, technological innovations, and scientific knowledge, fostering a collective understanding of the transboundary nature of heavy metal pollution.
The evolving landscape of heavy metal pollution underscores the need for a proactive and cooperative international approach [38]. The rising instances of transboundary pollution highlight the interconnectedness of environmental challenges and the imperative for shared responsibility. By developing robust international frameworks, nations can collectively address the complex dynamics of heavy metal pollution, ultimately safeguarding ecosystems, public health, and the well-being of communities across borders.

4.2. Heavy Metal Pollution Control: Novel Biotechnology of Shewanella oneidensis Biofilm

The management of heavy metal pollution encompasses a spectrum of techniques, ranging from physical and chemical methods [39,40,41] to novel biological approaches. Among these, biotechnology stands out as a promising avenue, and the utilization of Shewanella oneidensis biofilm represents a noteworthy advancement in the field. Shewanella oneidensis, a versatile bacterium, exhibits unique capabilities that extend beyond pollution control, offering potential applications in both electricity generation [42,43], as demonstrated by microbial fuel cells (MFCs) [44,45], and the immobilization of heavy metals.
Biotechnological strategies, particularly those involving Shewanella oneidensis biofilm, present a dual-functional approach to addressing heavy metal pollution [46]. Firstly, the cells of Shewanella oneidensis are known for their remarkable ability to conduct electrons. This property has been harnessed for electricity generation in microbial fuel cells, showcasing the versatile applications of this bacterium. The microbial fuel cell technology allows for the conversion of chemical energy present in organic or inorganic substances, including heavy metals, into electrical energy through the metabolic activities of microorganisms like Shewanella oneidensis. This not only provides an environmentally friendly means of electricity generation but also represents a novel approach to addressing heavy metal pollution by converting it into a potential energy source.
Simultaneously, Shewanella oneidensis biofilm exhibits robust performance in the immobilization of heavy metals [47,48]. The biofilm formation process creates a microenvironment conducive to complex interactions between microbial cells and heavy metal ions. Shewanella oneidensis cells within the biofilm matrix engage in intricate biochemical processes, including sorption, reduction, and precipitation, leading to the immobilization of heavy metals. This immobilization process is a crucial step in preventing the migration of heavy metals, reducing their bioavailability, and mitigating their impact on the surrounding environment.
The integration of Shewanella oneidensis biofilm in biotechnological strategies presents an exciting prospect for addressing heavy metal pollution. The dual benefits of electricity generation and heavy metal immobilization underscore the multifaceted potential of this approach. As research advances, the application of Shewanella oneidensis biofilm may contribute significantly to sustainable and innovative solutions for heavy metal pollution control.

4.3. Heavy Metal Pollution Governance with Big Data and Machine Learning

The current era is witnessing a transformative shift into the realms of big data and machine learning [49], where the successful implementation of technologies such as autonomous driving [50,51], facial recognition [52,53], predictive modeling of biological species distribution [54], and educational outcomes [55,56] has become a reality. In this era dominated by big data and machine learning, the governance of heavy metal pollution is poised to embrace new opportunities [57].
One notable opportunity lies in the establishment of a comprehensive global database, encompassing data on industrial facilities, mining operations, population demographics, economic indicators, concentrations of various heavy metal pollutants, and more [58]. This vast repository of information serves as the foundation for leveraging machine learning models to predict potential future occurrences of elevated heavy metal concentrations in specific regions. By harnessing the power of big data and machine learning, we can develop models that forecast where heightened levels of heavy metals might emerge, enabling proactive preparation and intervention.
The creation of a global database offers a panoramic view of diverse factors contributing to heavy metal pollution, allowing machine learning algorithms to discern intricate patterns and correlations [59]. These models can then extrapolate trends, identify potential hotspots, and predict areas at risk of increased heavy metal pollution. This predictive capability becomes invaluable for authorities, environmental agencies, and communities, offering them a tool to strategically allocate resources, implement preventive measures, and undertake targeted interventions.
Furthermore, machine learning models can be designed to adapt and evolve, continuously learning from new data inputs and refining their predictions over time [60]. This adaptability ensures that the heavy metal pollution governance strategies remain dynamic and responsive to changing environmental and industrial dynamics. It empowers decision-makers with timely and accurate insights, enabling them to stay ahead of potential pollution challenges.
Despite the promising opportunities presented by big data and machine learning, challenges and considerations exist [61]. Data privacy, ethical considerations, and the need for standardized data collection practices are critical aspects that require careful attention. Additionally, ensuring the accuracy and reliability of the machine learning models is paramount for their successful application in heavy metal pollution prediction and governance.
The era of big data and machine learning opens up unprecedented avenues for addressing and mitigating heavy metal pollution [62]. By harnessing the capabilities of global databases and advanced machine learning algorithms, we can usher in a new era of proactive and data-driven governance. This transformative approach not only enhances our understanding of heavy metal pollution dynamics but also equips us with the tools needed to anticipate, prevent, and effectively respond to environmental challenges on a global scale.

5. Conclusion

This bibliographic analysis highlights the pervasive challenge of heavy metal pollution affecting global populations, particularly in developing countries. The study underscores ongoing efforts to confront this threat through the exploration of new technologies. As biotechnology and computer applications become more widespread, there is optimism that scientists will pioneer innovative solutions to address the complexities of heavy metal pollution in the future. The findings emphasize the need for continued research and collaboration to harness the advancements in biotechnology and computing, ultimately paving the way for effective strategies to combat heavy metal pollution. This optimistic outlook encourages a collective commitment to environmental stewardship, urging scientists and policymakers to unite in the pursuit of cleaner, sustainable solutions for our shared global environment.

References

  1. Engwa, G.A.; Ferdinand, P.U.; Nwalo, F.N.; Unachukwu, M.N. Mechanism and health effects of heavy metal toxicity in humans. Poisoning in the modern world-new tricks for an old dog 2019, 10, 70-90. [CrossRef]
  2. Pujari, M.; Kapoor, D. Heavy metals in the ecosystem: Sources and their effects. In Heavy metals in the environment; Elsevier: 2021; pp. 1-7.
  3. Ciccu, R.; Ghiani, M.; Serci, A.; Fadda, S.; Peretti, R.; Zucca, A. Heavy metal immobilization in the mining-contaminated soils using various industrial wastes. Minerals Engineering 2003, 16, 187-192. [CrossRef]
  4. Mishra, S.; Bharagava, R.N.; More, N.; Yadav, A.; Zainith, S.; Mani, S.; Chowdhary, P. Heavy metal contamination: an alarming threat to environment and human health. Environmental biotechnology: For sustainable future 2019, 103-125. [CrossRef]
  5. Ding, Y. Heavy metal pollution and transboundary issues in ASEAN countries. Water Policy 2019, 21, 1096-1106. [CrossRef]
  6. Abdul-Wahab, S.; Marikar, F. The environmental impact of gold mines: pollution by heavy metals. Open engineering 2012, 2, 304-313. [CrossRef]
  7. Alengebawy, A.; Abdelkhalek, S.T.; Qureshi, S.R.; Wang, M.-Q. Heavy metals and pesticides toxicity in agricultural soil and plants: Ecological risks and human health implications. Toxics 2021, 9, 42. [CrossRef]
  8. Chowdhury, S.; Mazumder, M.A.J.; Al-Attas, O.; Husain, T. Heavy metals in drinking water: occurrences, implications, and future needs in developing countries. Science of the total Environment 2016, 569, 476-488. [CrossRef]
  9. Anyanwu, B.O.; Ezejiofor, A.N.; Igweze, Z.N.; Orisakwe, O.E. Heavy metal mixture exposure and effects in developing nations: an update. Toxics 2018, 6, 65. [CrossRef]
  10. Meena, R.A.A.; Sathishkumar, P.; Ameen, F.; Yusoff, A.R.M.; Gu, F.L. Heavy metal pollution in immobile and mobile components of lentic ecosystems—a review. Environmental Science and Pollution Research 2018, 25, 4134-4148. [CrossRef]
  11. Masri, S.; LeBrón, A.M.W.; Logue, M.D.; Valencia, E.; Ruiz, A.; Reyes, A.; Wu, J. Risk assessment of soil heavy metal contamination at the census tract level in the city of Santa Ana, CA: implications for health and environmental justice. Environmental Science: Processes & Impacts 2021, 23, 812-830. [CrossRef]
  12. Ahmad, W.; Alharthy, R.D.; Zubair, M.; Ahmed, M.; Hameed, A.; Rafique, S. Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Scientific Reports 2021, 11, 17006. [CrossRef]
  13. Naddafi, K.; Mesdaghinia, A.; Abtahi, M.; Hassanvand, M.S.; Beiki, A.; Shaghaghi, G.; Shamsipour, M.; Mohammadi, F.; Saeedi, R. Assessment of burden of disease induced by exposure to heavy metals through drinking water at national and subnational levels in Iran, 2019. Environmental Research 2022, 204, 112057. [CrossRef]
  14. Zhao, Q.; Wang, Y.; Cao, Y.; Chen, A.; Ren, M.; Ge, Y.; Yu, Z.; Wan, S.; Hu, A.; Bo, Q. Potential health risks of heavy metals in cultivated topsoil and grain, including correlations with human primary liver, lung and gastric cancer, in Anhui province, Eastern China. Science of the Total Environment 2014, 470, 340-347. [CrossRef]
  15. Tsuji, M.; Shibata, E.; Morokuma, S.; Tanaka, R.; Senju, A.; Araki, S.; Sanefuji, M.; Koriyama, C.; Yamamoto, M.; Ishihara, Y. The association between whole blood concentrations of heavy metals in pregnant women and premature births: The Japan Environment and Children's Study (JECS). Environmental research 2018, 166, 562-569. [CrossRef]
  16. Ji, Z.; Pei, Y. Bibliographic and visualized analysis of geopolymer research and its application in heavy metal immobilization: A review. Journal of environmental management 2019, 231, 256-267. [CrossRef]
  17. Xiao, P.; Zhou, Y.; Li, X.; Xu, J.; Zhao, C. Assessment of heavy metals in agricultural land: A literature review based on bibliometric analysis. Sustainability 2021, 13, 4559. [CrossRef]
  18. Durán-Sánchez, A.; Álvarez-García, J.; González-Vázquez, E.; Del Río-Rama, M.d.l.C. Wastewater management: Bibliometric analysis of scientific literature. Water 2020, 12, 2963. [CrossRef]
  19. Gavrilescu, M.; Demnerová, K.; Aamand, J.; Agathos, S.; Fava, F. Emerging pollutants in the environment: present and future challenges in biomonitoring, ecological risks and bioremediation. New biotechnology 2015, 32, 147-156. [CrossRef]
  20. Mani, D.; Kumar, C. Biotechnological advances in bioremediation of heavy metals contaminated ecosystems: an overview with special reference to phytoremediation. International journal of environmental science and technology 2014, 11, 843-872. [CrossRef]
  21. Das, S.; Das, S.; Ghangrekar, M.M. Efficacious bioremediation of heavy metals and radionuclides from wastewater employing aquatic macro-and microphytes. Journal of Basic Microbiology 2022, 62, 260-278. [CrossRef]
  22. Zhu, Y.; Fan, W.; Zhou, T.; Li, X. Removal of chelated heavy metals from aqueous solution: A review of current methods and mechanisms. Science of the Total Environment 2019, 678, 253-266. [CrossRef]
  23. Abdu, N.; Abdullahi, A.A.; Abdulkadir, A. Heavy metals and soil microbes. Environmental chemistry letters 2017, 15, 65-84. [CrossRef]
  24. Pietrucha-Urbanik, K.; Rak, J. Water, Resources, and Resilience: Insights from Diverse Environmental Studies. 2023, 15, 3965. [CrossRef]
  25. Chen, S.; Ding, Y. A bibliography study of Shewanella oneidensis biofilm. FEMS Microbiology Ecology 2023, 99, fiad124. [CrossRef]
  26. Chen, S.; Ding, Y. Tackling Heavy Metal Pollution: Evaluating Governance Models and Frameworks. Sustainability 2023, 15, 15863. [CrossRef]
  27. Hu, Y.; Sun, Z.; Wu, D. Analysis of hot topics in soil remediation research based on VOSviewer. 2019; p. 032098. [CrossRef]
  28. Han, R.; Zhou, B.; Huang, Y.; Lu, X.; Li, S.; Li, N. Bibliometric overview of research trends on heavy metal health risks and impacts in 1989–2018. Journal of Cleaner Production 2020, 276, 123249. [CrossRef]
  29. Covelo, E.F.; Andrade, M.L.; Vega, F.A. Heavy metal adsorption by humic umbrisols: selectivity sequences and competitive sorption kinetics. Journal of Colloid and Interface Science 2004, 280, 1-8. [CrossRef]
  30. Sharma, S.; Rana, S.; Thakkar, A.; Baldi, A.; Murthy, R.S.R.; Sharma, R.K. Physical, chemical and phytoremediation technique for removal of heavy metals. Journal of Heavy Metal Toxicity and Diseases 2016, 1, 1-15. [CrossRef]
  31. Hamdany, A.H.; Ding, Y.; Qian, S. Cementitious Composite Materials for Self-Sterilization Surfaces. ACI Materials Journal 2022, 119, 197-210. [CrossRef]
  32. Hamdany, A.H.; Ding, Y.; Qian, S. Mechanical and antibacterial behavior of photocatalytic lightweight engineered cementitious composites. Journal of Materials in Civil Engineering 2021, 33, 04021262. [CrossRef]
  33. Hamdany, A.H.; Ding, Y.; Qian, S. Visible light antibacterial potential of graphene-TiO2 cementitious composites for self-sterilization surface. Journal of Sustainable Cement-Based Materials 2023, 12, 972-982. [CrossRef]
  34. Hamdany, A.H.; Ding, Y.; Qian, S. Graphene-Based TiO2 Cement Composites to Enhance the Antibacterial Effect of Self-Disinfecting Surfaces. Catalysts 2023, 13, 1313. [CrossRef]
  35. Ilyin, I.; Rozovskaya, O.; Travnikov, O.; Aas, W.; Hettelingh, J.P.; Reinds, G.J. Heavy metals: transboundary pollution of the environment. EMEP Status report 2003, 2, 40.
  36. Gomez-Ramirez, P.; Shore, R.F.; Van Den Brink, N.W.; Van Hattum, B.; Bustnes, J.O.; Duke, G.; Fritsch, C.; García-Fernández, A.J.; Helander, B.O.; Jaspers, V. An overview of existing raptor contaminant monitoring activities in Europe. Environment International 2014, 67, 12-21. [CrossRef]
  37. Perrez, F.X. The role of the United Nations Environment Assembly in emerging issues of international environmental law. Sustainability 2020, 12, 5680. [CrossRef]
  38. Ahonen, M.; Kahru, A.; Ivask, A.; Kasemets, K.; Kõljalg, S.; Mantecca, P.; Vinković Vrček, I.; Keinänen-Toivola, M.M.; Crijns, F. Proactive approach for safe use of antimicrobial coatings in healthcare settings: opinion of the COST action network AMiCI. International journal of environmental research and public health 2017, 14, 366. [CrossRef]
  39. Ding, Y.; Liu, B.; Shen, X.; Zhong, L.; Li, X. Foam-assisted delivery of nanoscale zero valent iron in porous media. Journal of Environmental Engineering 2013, 139, 1206-1212. [CrossRef]
  40. Nie, N.; Ding, Y.; Li, X. Preparation of zeolite and zero valent iron composite for cleanup of hexavalent contamination in water. China Environmental Science 2013, 33, 443-447. [CrossRef]
  41. Zhang, B.; Jiang, Y.; Ding, Y.; Zhang, J.; Balasubramanian, R. Iron-catalyzed synthesis of biowaste-derived magnetic carbonaceous materials for environmental remediation applications. Separation and Purification Technology 2022, 295, 121321. [CrossRef]
  42. Yang, Y.; Ding, Y.; Hu, Y.; Cao, B.; Rice, S.A.; Kjelleberg, S.; Song, H. Enhancing bidirectional electron transfer of Shewanella oneidensis by a synthetic flavin pathway. ACS synthetic biology 2015, 4, 815-823. [CrossRef]
  43. Liu, T.; Yu, Y.Y.; Deng, X.P.; Ng, C.K.; Cao, B.; Wang, J.Y.; Rice, S.A.; Kjelleberg, S.; Song, H. Enhanced Shewanella biofilm promotes bioelectricity generation. Biotechnology and bioengineering 2015, 112, 2051-2059. [CrossRef]
  44. Zhao, C.e.; Wu, J.; Ding, Y.; Wang, V.B.; Zhang, Y.; Kjelleberg, S.; Loo, J.S.C.; Cao, B.; Zhang, Q. Hybrid conducting biofilm with built-in bacteria for high-performance microbial fuel cells. ChemElectroChem 2015, 2, 654-658. [CrossRef]
  45. Zhao, C.-e.; Chen, J.; Ding, Y.; Wang, V.B.; Bao, B.; Kjelleberg, S.; Cao, B.; Loo, S.C.J.; Wang, L.; Huang, W. Chemically functionalized conjugated oligoelectrolyte nanoparticles for enhancement of current generation in microbial fuel cells. ACS Applied Materials & Interfaces 2015, 7, 14501-14505. [CrossRef]
  46. Ding, Y.; Peng, N.; Du, Y.; Ji, L.; Cao, B. Disruption of putrescine biosynthesis in Shewanella oneidensis enhances biofilm cohesiveness and performance in Cr (VI) immobilization. Applied and environmental microbiology 2014, 80, 1498-1506. [CrossRef]
  47. Ding, Y.; Zhou, Y.; Yao, J.; Szymanski, C.; Fredrickson, J.; Shi, L.; Cao, B.; Zhu, Z.; Yu, X.-Y. In situ molecular imaging of the biofilm and its matrix. Analytical chemistry 2016, 88, 11244-11252. [CrossRef]
  48. Ding, Y.; Zhou, Y.; Yao, J.; Xiong, Y.; Zhu, Z.; Yu, X.-Y. Molecular evidence of a toxic effect on a biofilm and its matrix. Analyst 2019, 144, 2498-2503. [CrossRef]
  49. Rai, R.; Tiwari, M.K.; Ivanov, D.; Dolgui, A. Machine learning in manufacturing and industry 4.0 applications. 2021, 59, 4773-4778. [CrossRef]
  50. Bachute, M.R.; Subhedar, J.M. Autonomous driving architectures: insights of machine learning and deep learning algorithms. Machine Learning with Applications 2021, 6, 100164. [CrossRef]
  51. Shafaei, S.; Kugele, S.; Osman, M.H.; Knoll, A. Uncertainty in machine learning: A safety perspective on autonomous driving. 2018; pp. 458-464.
  52. Kaur, P.; Krishan, K.; Sharma, S.K.; Kanchan, T. Facial-recognition algorithms: A literature review. Medicine, Science and the Law 2020, 60, 131-139. [CrossRef]
  53. Almeida, D.; Shmarko, K.; Lomas, E. The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks. AI and Ethics 2022, 2, 377-387. [CrossRef]
  54. Chen, S.; Ding, Y. Machine Learning and Its Applications in Studying the Geographical Distribution of Ants. Diversity 2022, 14, 706. [CrossRef]
  55. Chen, S.; Ding, Y. A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools. Social Sciences 2023, 12, 118. [CrossRef]
  56. Chen, S.; Ding, Y. Assessing the Psychometric Properties of STEAM Competence in Primary School Students: A Construct Measurement Study. Journal of Psychoeducational Assessment 2023, 41, 796-810. [CrossRef]
  57. Ge, Z.; Song, Z.; Ding, S.X.; Huang, B. Data mining and analytics in the process industry: The role of machine learning. Ieee Access 2017, 5, 20590-20616. [CrossRef]
  58. Chen, C.L.P.; Zhang, C.-Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences 2014, 275, 314-347. [CrossRef]
  59. Hafsa, N.; Rushd, S.; Al-Yaari, M.; Rahman, M. A generalized method for modeling the adsorption of heavy metals with machine learning algorithms. Water 2020, 12, 3490. [CrossRef]
  60. Li, X.; Yang, Y.; Yang, J.; Fan, Y.; Qian, X.; Li, H. Rapid diagnosis of heavy metal pollution in lake sediments based on environmental magnetism and machine learning. Journal of Hazardous Materials 2021, 416, 126163. [CrossRef]
  61. Yaseen, Z.M. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere 2021, 277, 130126. [CrossRef]
  62. L’heureux, A.; Grolinger, K.; Elyamany, H.F.; Capretz, M.A.M. Machine learning with big data: Challenges and approaches. Ieee Access 2017, 5, 7776-7797. [CrossRef]
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